from hashlib import sha1
import altair as alt
import pandas as pd
import numpy as np
from hashlib import sha1
alt.data_transformers.disable_max_rows()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 2
      1 from hashlib import sha1
----> 2 import altair as alt
      3 import pandas as pd
      4 import numpy as np

ModuleNotFoundError: No module named 'altair'

Use read_csv from pandas to load the data from the data folder and assign it to a variable named gapminder_df.

Make sure to parse any time columns using the parse_dates argument.

gapminder_df = pd.read_csv("C:/Users/sindi/Downloads/world-data-gapminder.csv", parse_dates=['year'])
gapminder_df.head()

	country	year	population	region	sub_region	income_group	life_expectancy	income	children_per_woman	child_mortality	pop_density	co2_per_capita	years_in_school_men	years_in_school_women
0	Afghanistan	1800-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.0	469.0	NaN	NaN	NaN	NaN
1	Afghanistan	1801-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.0	469.0	NaN	NaN	NaN	NaN
2	Afghanistan	1802-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.0	469.0	NaN	NaN	NaN	NaN
3	Afghanistan	1803-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.0	469.0	NaN	NaN	NaN	NaN
4	Afghanistan	1804-01-01	3280000	Asia	Southern Asia	Low	28.2	603	7.0	469.0	NaN	NaN	NaN	NaN

plot_a = alt.Chart(gapminder_df).mark_line().encode(
    x='year', 
    y='population', 
    color='region').properties(
    title='Plot A',width=300, height=200)

plot_b = alt.Chart(gapminder_df).mark_line().encode(
    x='year', 
    y='sum(population)', 
    color='region').properties(
    title='Plot B',width=300, height=200)

plot_c = alt.Chart(gapminder_df).mark_area().encode(
    x='year', 
    y='sum(population)', 
    color='region').properties(
    title='Plot C',width=300, height=200)

plot_d = alt.Chart(gapminder_df).mark_circle().encode(
    alt.X('year', scale=alt.Scale(zero=False)),
    alt.Y('population'),
    alt.Color('region')).properties(
    title='Plot D',width=300, height=200)

alt.vconcat(alt.hconcat(
    plot_a, plot_b
).resolve_scale(
    color='independent'),  
alt.hconcat(
    plot_c, plot_d
).resolve_scale(
    color='independent'))
plot_a

plot_b

plot_d

As we can see, the dataframe is difficult to digest

gapminder_every20 = gapminder_df[gapminder_df["year"].isin(['1918','1938','1958','1978','1998','2018'])]
gapminder_every20

	country	year	population	region	sub_region	income_group	life_expectancy	income	children_per_woman	child_mortality	pop_density	co2_per_capita	years_in_school_men	years_in_school_women
118	Afghanistan	1918-01-01	5700000	Asia	Southern Asia	Low	7.89	849	7.00	468.0	NaN	NaN	NaN	NaN
138	Afghanistan	1938-01-01	6900000	Asia	Southern Asia	Low	31.30	963	7.33	443.0	NaN	NaN	NaN	NaN
158	Afghanistan	1958-01-01	8680000	Asia	Southern Asia	Low	37.20	1180	7.48	375.0	13.30	0.0380	NaN	NaN
178	Afghanistan	1978-01-01	13200000	Asia	Southern Asia	Low	45.00	1190	7.45	258.0	20.30	0.1630	1.67	0.27
198	Afghanistan	1998-01-01	18900000	Asia	Southern Asia	Low	50.10	956	7.62	137.0	28.90	0.0552	2.76	0.55
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
38901	Zimbabwe	1938-01-01	2500000	Africa	Sub-Saharan Africa	Low	34.80	1180	6.75	345.0	NaN	1.1100	NaN	NaN
38921	Zimbabwe	1958-01-01	3520000	Africa	Sub-Saharan Africa	Low	53.00	1950	7.04	159.0	9.09	NaN	NaN	NaN
38941	Zimbabwe	1978-01-01	6700000	Africa	Sub-Saharan Africa	Low	56.70	2250	7.27	108.0	17.30	1.3900	6.20	4.55
38961	Zimbabwe	1998-01-01	11900000	Africa	Sub-Saharan Africa	Low	49.10	2750	4.16	95.9	30.70	1.2000	8.80	7.39
38981	Zimbabwe	2018-01-01	16900000	Africa	Sub-Saharan Africa	Low	60.20	1950	3.61	55.5	43.70	NaN	NaN	NaN

1068 rows × 14 columns

The combined plot is rather cluttered

family_plot = alt.Chart(gapminder_every20).mark_circle().encode(
alt.X("children_per_woman", title="Children per woman"),
alt.Y("child_mortality", title="Child mortality"), 
alt.Color("income_group", title="Income Group")

).properties(title="Child mortality vs Children born per woman")

family_plot

It is much easier to make sense of when faceted

family_plot_faceted = family_plot.facet("year", columns=3)
family_plot_faceted

When the plots are faceted

Some of the equations used in the analysis

sample mean \begin{equation} \bar{y} = \frac{1}{n}\sum_{i=1}^n y_i \end{equation}

Here is the sample variance: \begin{equation} \sigma^2 = \frac{\sum\limits_{i=1}^{n}(y_i – \bar{y})^2} {n – 1} \end{equation}